论文标题
神经句法预订，用于受控释义
Neural Syntactic Preordering for Controlled Paraphrase Generation
论文作者
论文摘要
释义自然语言句子是一个多方面的过程：它可能涉及更换单个单词或简短的短语，内容的本地重新安排或高级重组（例如局部化或钝化）。过去的方法难以以可解释的方式覆盖这种释义可能性的空间。我们的工作受到机器翻译的预订文献的启发，使用句法转换来轻轻地“重新排序”''源句子，并指导我们的神经释义模型。首先，鉴于输入句子，我们得出了一组可行的句法重排，使用一个模型，该模型在一个模型中使用编码的部分。每个提议的重新安排都会产生一系列位置嵌入，这鼓励我们的最终编码器解码器模型以特定的顺序处理源单词，这是我们的自动和人类的评估。
Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization. Past approaches struggle to cover this space of paraphrase possibilities in an interpretable manner. Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly "reorder'' the source sentence and guide our neural paraphrasing model. First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model. This model operates over a partially lexical, partially syntactic view of the sentence and can reorder big chunks. Next, we use each proposed rearrangement to produce a sequence of position embeddings, which encourages our final encoder-decoder paraphrase model to attend to the source words in a particular order. Our evaluation, both automatic and human, shows that the proposed system retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases
